Paper: Selecting Optimal Feature Template Subset for CRFs

ACL ID W10-4113
Title Selecting Optimal Feature Template Subset for CRFs
Venue Joint Conference on Chinese Language Processing
Session Main Conference
Year 2010
Authors

Conditional Random Fields (CRFs) are the state-of-the-art models for sequential labe- ling problems. A critical step is to select optimal feature template subset before em- ploying CRFs, which is a tedious task. To improve the efficiency of this step, we pro- pose a new method that adopts the maxi- mum entropy (ME) model and maximum entropy Markov models (MEMMs) instead of CRFs considering the homology be- tween ME, MEMMs, and CRFs. Moreover, empirical studies on the efficiency and ef- fectiveness of the method are conducted in the field of Chinese text chunking, whose performance is ranked the first place in task two of CIPS-ParsEval-2009.